Deep Analysis and Detection of Skin Disease using YOLO-NAS Algorithm

The project aims to develop a machine learning system using the YOLO-NAS algorithm to identify and classify skin diseases. The system is trained on a skin disease dataset featuring conditions like cellulitis, impetigo, athlete's foot, nail fungus, ringworm, cutaneous larva migrans, chickenpox, and shingles. YOLO-NAS enhances both speed and accuracy by optimizing its architecture through neural architecture search. Real-time detection provides immediate feedback to assist healthcare professionals in evaluating skin conditions. The methodology includes data preprocessing, model training, validation, and testing, with augmented and balanced datasets to ensure robustness. The model achieves 78% accuracy, evaluated using precision, recall, and F1 score. The project aims to revolutionize skin disease diagnosis by providing high accuracy and real-time feedback, helping clinicians make swift decisions. Additionally, the model's predictions will be made transparent to users, ensuring interpretability. By improving efficiency and delivering quick responses, this project strives to enhance skin disease management and improve clinical outcomes.